1. Field of the Invention
The present invention relates generally to evaluation of social networks and in particular to computer-implemented evaluation of social networks. Still more particularly, the present invention relates to a method, system and computer program product for providing automated anomaly detection within social networks.
2. Description of the Related Art
Social Network Analysis (SNA) is a technique utilized by anthropologists, psychologists, intelligence analysts, and others to analyze social interaction(s) and/or to investigate the organization of and relationships within formal and informal networks such as corporations, filial groups, or computer networks.
SNA typically represents a social network as a graph (referred to as a social interaction graph, communication graph, activity graph, or sociogram). In its simplest form, a social network graph contains nodes representing actors (generally people or organizations) and edges representing relationships or communications between the actors. In contrast with databases and spreadsheets, which tend to facilitate reasoning over the characteristics of individual actors, graph-based representations facilitate reasoning over relationships between actors.
In conventional analysis of these graphs most analysts search and reason over the graphs visually, and the analysts are able to reason about either the individual actors or the network as a whole through graph-theoretic approaches. Social Network Analysis (SNA) was developed to describe visual concepts and truths between the observed relationships/interactions. In conventional social network analysis, most graphs are analyzed by visual search and reasoning over the graphs. Analysts are able to reason about either individual actors or the network as a whole through various approaches and theories about structure, such as the small-worlds conjecture. Thus, SNA describes visual concepts and truths between the observed relationships and actors.
Analysts use certain key terms or characterizations to refer to how actors appear to behave in a social network, such as gatekeeper, leader, and follower. Designating actors as one of these can be done by straightforward visual analysis for static (i.e., non-time varying graphs of past activity). However, some characterizations can only be made by observing a graph as the graph changes over time. This type of observation is significantly harder to do manually.
Thus, SNA metrics were developed to distill certain aspects of a graph's structure into numbers that can be computed automatically. Metrics can be computed automatically and repetitively for automated inspection. Decision algorithms, such as neural networks or hidden Markov models may then make the determination if a given actor fills a specific role. These algorithms may be taught to make the distinction with labeled training data.
With conventional SNA techniques, there is presently no convenient way to (a) determine the functional roles of individuals and organizations in a social network (e.g., gatekeepers, leaders, followers) and (b) diagnose network-wide conditions (e.g., too much centralization of authority, inefficient communication paths, etc.). These processes and others have not been addressed with conventional SNA techniques and SNA systems, which typically rely on manual observations by the analyst with no application to anomalies within the data.
Also, where traditional metric analysis fails is when there either is (a) no labeled training data or (b) not an accurate enough model to determine what is normal or abnormal. Thus, the present invention recognizes that a need exists for a more robust and automatic method for enabling analysts to computationally analyze social networks to determine anomalies within data, even when there is no pre-established norm with which to compare the data.